Top Machine Learning Jobs in Krakow

Lead software engineering efforts for Samsara’s External Platform EMEA, building scalable distributed systems, APIs, reporting, administration, integrations, and business-critical infrastructure. Lead technical discussions, influence architecture and roadmap, deliver full-stack features, maintain production systems, and support customer-focused iteration. Mentor engineers, hire and develop high-performing teams, and contribute to machine learning-powered products and long-running projects.

Assess and manage cybersecurity risks across AI systems, models, data pipelines, and supporting environments. Conduct AI security assessments, architecture reviews, threat modeling, and risk assessments; define lifecycle controls, standards, and guardrails; and support governance, monitoring, incident response, and remediation. The consultant will advise financial services clients on secure AI adoption while collaborating with security, data, engineering, architecture, risk, and business stakeholders. The role requires a one-year relocation to Qatar.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Own the roadmap and delivery of data products supporting credit, BNPL, underwriting, fraud, acquiring, and payments decisions. Translate business, risk, and ML needs into buildable specifications, align Data Engineering and ML teams, define outcome metrics, and lead up to three Data Consultants. Establish AI-first, agentic ways of working with appropriate guardrails, evaluations, human oversight, and cost controls. Partner across technical, commercial, and risk stakeholders while managing capacity and priorities across multiple product lines.

Supervise middle office activities including trade and cash review, settlement processing, trade booking accuracy, end-of-day pricing, and client deliverables. Resolve position and cash breaks while addressing client system and trade queries. Collaborate with NAV, IT, risk, investor services, and business development teams, and support technology implementations that improve client processes. The role requires strong OTC and listed products knowledge, Excel skills, accuracy, and effective multitasking.

Own the product roadmap and portfolio prioritization for vehicle data and telematics products across EMEA. Lead customer discovery, solution validation, product reporting, feedback loops, and strategic initiatives from conception through launch and continuous improvement. Collaborate with designers, developers, data scientists, technical project managers, customers, and executives. Mentor product managers, communicate product strategy, and use engagement metrics and KPIs to drive data-informed decisions.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Designs and deploys scalable AI solutions for enterprise customers. Responsibilities include gathering technical requirements, building integrations and prototypes, leading architecture workshops, troubleshooting production issues, documenting reusable practices, and collaborating with Product and Engineering teams to improve the platform.

Design, prototype, and optimize software systems for machine-learning and agentic applications. Build training and inference pipelines across edge devices, GPU servers, HPC clusters, and cloud environments. Evaluate ML frameworks, serving technologies, accelerators, and development tools; collaborate with researchers, engineers, universities, vendors, and business units. Disseminate results through publications, patents, and technical reports while providing technical consultation.

Design, develop, and deploy agentic AI systems and intelligent automation solutions on Azure. Build LLM, RAG, and machine learning applications; create data workflows with Databricks and PySpark; implement CI/CD, monitoring, security, and observability; optimize production performance and costs; and collaborate with engineering, product, and business teams.

Own the vision, roadmap, commercialization, and executive strategy for Kaltura’s AI-powered learning and enablement platform. Lead 0-to-1 MVP delivery and post-MVP expansion across AI course generation, conversational tutoring, personalized learning, simulations, and enterprise foundations. Integrate acquired products and agentic AI capabilities while partnering with engineering and GTM teams on architecture, pricing, packaging, positioning, and enterprise sales. The role requires deep B2B SaaS product leadership, enterprise learning platform experience, production AI/ML delivery, and strong technical and commercial judgment.

Leads the data and AI function for a new fintech platform, owning strategy, organization, budget, data engineering, ML/MLOps, data products, feature stores, agentic systems, governance, and real-time credit and risk decisioning. Builds the function from scratch, establishes production AI standards, oversees platform reliability and observability, partners on architecture and risk, and reports to executive and board-level stakeholders.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Own the strategy and execution of integrated B2B marketing campaigns for enterprise AI solutions and a self-service platform. Drive pipeline, product adoption, acquisition, and category awareness through ABM, content, events, paid media, lifecycle, social, partnerships, PR, and sales activation. Coordinate internal teams and agencies, optimize messaging and conversion paths, and measure performance across account engagement, pipeline, product adoption, and awareness.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Design and deliver internal systems, Go services, APIs, data models, dashboards, visualizations, and LLM-powered workflow features. The role includes writing specifications, estimating work, code reviews, testing, documentation, security, performance, accessibility, and collaboration with nontechnical stakeholders. The engineer will handle sensitive people data, mentor colleagues, and improve team practices in a globally distributed environment.

Own the anti-fraud product roadmap across signup, payments, account protection, compute-abuse prevention, risk scoring, manual review, customer verification, and appeals. Partner with Trust & Safety, Engineering, Data Science, Analytics, and Legal to define detection rules, models, incident responses, experiments, and compliance processes. Monitor fraud losses, chargebacks, false positives, review throughput, and detection speed while minimizing customer friction.

Design, implement, optimize, and maintain complex large-scale software systems for airline network planning. Collaborate with globally distributed teams, solve architectural and algorithmic challenges, improve development processes and tooling, incorporate AI into workflows, mentor engineers, and drive software quality, stability, and operational excellence.

Develop machine learning models, detection rules, and data-driven solutions for web security products. Analyze massive customer datasets, improve detection accuracy, enhance production data processes, troubleshoot distributed systems, and collaborate with engineering and data science teams to address customer challenges and guide product improvements.

Lead and develop Customer Success Managers across Southern Europe, especially France and Spain. Ensure enterprise customers successfully launch and derive measurable value from the product, build executive and technical stakeholder relationships, translate product capabilities and performance data into business outcomes, establish scalable customer success processes, and handle complex escalations.

Develop quantitative models for sports pricing, odds compilation, and risk management. Analyze large datasets, identify trading insights, deploy models with engineers, monitor performance, conduct back-testing, and continuously improve model accuracy. Collaborate with data scientists and engineers to align models with business needs. The role requires Python, sports betting market knowledge, statistical modeling, and experience with odds and margin optimization.

Design and implement data-driven applications and end-to-end data systems for clients. Responsibilities span cloud engineering, infrastructure, data engineering, ETL, machine learning, and data science. The role involves evaluating and presenting results, applying software engineering best practices, collaborating with data scientists and stakeholders, and delivering solutions through agile, iterative development.

Develop and deploy machine learning models for retrieval, reranking, indexing, search relevance, crawling, and content understanding. Build large-scale embedding and retrieval systems, evaluation pipelines, and quality metrics for agent-native search. Integrate models into high-throughput production services while balancing latency, quality, and cost. Collaborate on architecture and product decisions involving search, retrieval, and LLM-based systems.

Lead product marketing for the French market by developing localized positioning, messaging, sales enablement, launch campaigns, and customer insights. Partner cross-functionally with product, sales, marketing, engineering, design, communications, and partnerships teams. Track adoption and market performance, identify growth opportunities, and lead product marketing strategy for a major Paris customer event. The role requires French and English fluency, B2B technology marketing experience, and travel within France and internationally.